Chinese-CLIP
PyTorch_CIFAR10
| Chinese-CLIP | PyTorch_CIFAR10 | |
|---|---|---|
| 1 | 1 | |
| 3,655 | 601 | |
| 7.6% | - | |
| 7.6 | 0.0 | |
| 5 months ago | 11 months ago | |
| Python | Python | |
| MIT License | MIT License |
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Chinese-CLIP
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Meet ‘Chinese CLIP,’ An Implementation of CLIP Pretrained on Large-Scale Chinese Datasets with Contrastive Learning
Chinese-CLIP is open-sourced on https://github.com/OFA-Sys/Chinese-CLIP , we are working on applying it on more downstreaming tasks requiring cross-modal alignment!
PyTorch_CIFAR10
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A wild question? Why CNNs are not aware of visual quality? [D]
However, when I make inference using a pre-trained CIFAR10 model link the confidence scores of the predicted classes are very high.
What are some alternatives?
dream-creator - Quickly and easily create / train a custom DeepDream model
HDF5ImageMarker - Utility to making datasets of images and points coordinates that have been marked up on these images by user
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Queryable - Run OpenAI's CLIP model on iOS to search photos.
JEM - Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
autodistill-metaclip - MetaCLIP module for use with Autodistill.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
open_clip - An open source implementation of CLIP.
PatrickStar - PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP and democratizes AI for everyone.